# PVBM: A Python Vasculature Biomarker Toolbox Based On Retinal Blood Vessel Segmentation

arXiv: https://arxiv.org/abs/2208.00392 · HTML: https://mlsanity.com/paper/2208.00392/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 50 references that are in the map.
- First posted on arXiv 31 Jul 2022.

## Builds on (most cited first, 1 of 1)

- [FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading](https://mlsanity.com/paper/2205.01676/) (2022)

## Built on by (most cited first, 5 of 5)

- [LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images](https://mlsanity.com/paper/2309.05780/) (2023)
- [Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling](https://mlsanity.com/paper/2503.01190/) (2025)
- [retinalysis-vascx: An explainable software toolbox for the extraction of retinal vascular biomarkers](https://mlsanity.com/paper/2602.08580/) (2026)
- [Diffusion Autoencoder for Unsupervised Artifact Restoration in Handheld Fundus Images](https://mlsanity.com/paper/2604.15723/) (2026)
- [Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images](https://mlsanity.com/paper/2609.16889/) (2026)

## Abstract (arXiv)

Introduction: Blood vessels can be non-invasively visualized from a digital fundus image (DFI). Several studies have shown an association between cardiovascular risk and vascular features obtained from DFI. Recent advances in computer vision and image segmentation enable automatising DFI blood vessel segmentation. There is a need for a resource that can automatically compute digital vasculature biomarkers (VBM) from these segmented DFI. Methods: In this paper, we introduce a Python Vasculature BioMarker toolbox, denoted PVBM. A total of 11 VBMs were implemented. In particular, we introduce new algorithmic methods to estimate tortuosity and branching angles. Using PVBM, and as a proof of usability, we analyze geometric vascular differences between glaucomatous patients and healthy controls. Results: We built a fully automated vasculature biomarker toolbox based on DFI segmentations and provided a proof of usability to characterize the vascular changes in glaucoma. For arterioles and venules, all biomarkers were significant and lower in glaucoma patients compared to healthy controls except for tortuosity, venular singularity length and venular branching angles. Conclusion: We have automated the computation of 11 VBMs from retinal blood vessel segmentation. The PVBM toolbox is made open source under a GNU GPL 3 license and is available on physiozoo.com (following publication).